Job Description
We are seeking a skilled and visionary technologist to spearhead the design, development, and deployment of advanced machine learning solutions. In this leadership role, you will guide a team of engineers, contribute hands-on to critical technical challenges, and collaborate with cross-functional teams to deliver impactful AI products.
This role is ideal for someone who thrives at the intersection of innovation, engineering rigor, and business value.
Key Responsibilities :
- Lead the architecture, development, and deployment of machine learning models and AI systems across a range of use cases.
- Design, train, and optimize supervised, unsupervised, and deep learning models using frameworks like PyTorch, TensorFlow, and XGBoost.
- Coach and mentor a team of ML engineers and data scientists; foster a culture of innovation, ownership, and continuous learning.
- Drive planning, execution, and delivery of AI / ML projects, ensuring alignment with business objectives and technical feasibility.
- Architect scalable, secure, and high-performance ML pipelines and services using cloud-native tools and MLOps best practices.
- Work closely with product managers, data engineers, and DevOps teams to translate business problems into AI-driven solutions.
- Establish standards for model quality, reproducibility, documentation, versioning, and monitoring.
- Stay current with research and industry trends in ML / AI, evaluate new tools, and introduce state-of-the-art solutions where applicable.
Required Skills and Qualifications :
Bachelor's or Master's in Computer Science, Machine Learning, Data Science, or related technical field.6–10+ years of experience in software engineering or AI / ML, with at least 2+ years in a technical leadership role.Strong programming skills in Python and experience with ML libraries such as Scikit-learn, TensorFlow, PyTorch, Hugging Face.Deep understanding of ML fundamentals : feature engineering, model evaluation, optimization, and deployment.Proficiency in designing and building data pipelines, real-time processing, and model inference systems.Experience with cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI / CD pipelines.Familiarity with MLOps tools (e.g., MLflow, DVC, Airflow, SageMaker) and vector databases (e.g., FAISS, Pinecone).Preferred Qualifications :
Hands-on experience with LLMs, RAG pipelines, or generative AI applications.Familiarity with agentic AI frameworks (LangChain, CrewAI, AutoGPT).Domain expertise in fintech, healthtech, HR tech, or industrial automation.Contributions to open-source AI / ML projects or published research.Knowledge of responsible AI practices, explainability (XAI), and model governance.Soft Skills :
Strong leadership and team-building skills.Clear and persuasive communication with both technical and non-technical stakeholders.Strategic thinker with attention to detail and a bias for action.